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To study this phenomenon, SocioHack is introduced, a sandbox of 72 societal environments, and it is found that within these environments, reward hacking naturally emerges and leads to regulatory loophole discovery.
GraphMind, a model that jointly processes micro- and macro-level structures for novelty prediction and rationale generation, is proposed that significantly outperforms baseline LLMs in both novelty score prediction and rationale generation.
Scientific papers may relate by problem, method, result, or contribution, but document-level retrievers collapse these into a single similarity score without saying why they are related. Citation- and similarity-based retrieval alone also confines search to the neighbourhood of what is already known, whereas generative...
Assessing scientific novelty is inherently complex, requiring evaluation of both a paper's internal structure and its contribution within the broader research landscape. Existing large language model (LLM) approaches often rely on surface-level similarity or citation retrieval, overlooking the integration between a pap...
The AppraiSal benchmark is introduced, containing 996 emotional support conversations with human-annotated mental states, including salient cognitive appraisal dimensions, and PRISM, a multi-agent probabilistic framework grounded in Bayesian Inverse Planning, designed to improve LLMs'ability to identify context-specifi...
GRADE (GRAdient Dynamics for knowlEdge gap detection), which quantifies the knowledge gap via the cross-layer rank ratio of the gradient to that of the corresponding hidden state subspace, motivated by the property of gradients as estimators of the required knowledge updates for a given target.
This work analyses the linearity and quality of MGT representations and shows that simple linear probes outperform a wide range of detectors while being substantially more sample-efficient, and demonstrates the potential of linear probes as as robust and sample-efficient MGT detectors.
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